Commercial search engine providers (e.g., Microsoft® Bing™) analyze submitted queries and respond with suggested web pages that relevant and useful. In order to quickly and continuously improve search engine experience, these providers mine the data from millions of participating users who submit queries, initiate searches and select web pages on search results. By mining informational needs from logged user search histories and browsing histories, the search engine providers are able to analyze current search engine performance and enhance future search result quality with improved techniques for mapping topics to queries, locating matching documents, displaying the documents on a search result page and so forth.
During a search process, the user may issue a series of queries and click several web page URLs in order to locate desired information. Therefore, evaluating search engine performance based on single query does not provide enough insight into user logic and other issues related to the search engine experience. In addition, the search engine providers employ various techniques for analyzing the search and browsing histories, but these techniques do not capture enough details regarding the search engine experience. Essentially, because the search engine providers are unable to accurately model user behavior during the search engine experience, these providers cannot substantially improve the search engines. The providers desire techniques that holistically analyze search engine performance for the purpose of creating search engines that produce more useful and relevant search results.